Free Cooling-Aware Dynamic Power Management for Green Datacenters

David Atienza Alonso, Jungsoo Kim, Martino Ruggiero
IEEE Press, 2012
Article de conférence

Free cooling, i.e., directly using outside cold air and/or water to cool down datacenters, can provide significant power savings of datacenters. However, due to the limited cooling capability, which is tightly coupled with climate conditions, free cooling is currently used only in limited locations (e.g., North Europe) and periods of the year. Moreover, the applicability of free cooling is further restricted along with the conservative assumption on workload characteristics and the virtual machine (VM) consolidation technique as they require to provision higher cooling capability. This paper presents a dynamic power management scheme, which extends the applicability of free cooling by judiciously consolidating VMs exploiting time-varying workload characteristics of datacenter as well as climate conditions, in order to minimize the power consumption of the entire datacenter while satisfying service-level agreement (SLA) requirements. Additionally, we propose the use of a receding horizon control scheme in order to prevent frequent cooling mode transitions. Experimental results show that the proposed solution provides up to 25.7% power savings compared to conventional free cooling decision schemes, which uses free cooling only when the outside temperature is lower than predefined threshold temperature.

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Concepts associés

Chargement

Publications associées

Chargement

David Atienza Alonso, Jungsoo Kim, Martino Ruggiero
IEEE Press, 2012
Article de conférence

Résumé

Official source

https://infoscience.epfl.ch/record/181265?ln=fr

À propos de ce résultat

Concepts associés (6)

A green data center, or sustainable data center, is a service facility which utilizes energy-efficient technologies. They do not contain obsolete systems (such as inactive or underused servers), and

thumb|Machine virtuelle des assistants personnels Palm. En informatique, une machine virtuelle (anglais virtual machine, abr. VM) est d'un appareil informatique créée par un logiciel d'émulation ou

vignette|Centre de traitement de données. Un centre de données (en anglais data center ou data centre), ou centre informatique est un lieu (et un service) où sont regroupés les équipements constituan

Afficher plus

Concepts associés

Chargement

Publications associées (5)

Publications associées

Chargement

Correlation-Aware Virtual Machine Allocation for Energy-Efficient Datacenters

David Atienza Alonso, Jungsoo Kim, Marcel Ledergerber, Martino Ruggiero

Server consolidation plays a key role to mitigate the continuous power increase of datacenters. The recent advent of scale-out applications (e.g., web search, MapReduce, etc.) necessitate the revisit of existing server consolidation solutions due to distinctively different characteristics compared to traditional high-performance computing (HPC), i.e., user interactive, latency critical, and operations on large data sets split across a number of servers. This paper presents a power saving solution for datacenters that especially targets the distinctive characteristics of the scale-out applications. More specifically, we take into account correlation information of core utilization among virtual machines (VMs) in server consolidation to lower actual peak server utilization. Then, we utilize this reduction to achieve further power savings by aggressively-yet-safely lowering the server operating voltage and frequency level. We have validated the effectiveness of the proposed solution using 1) multiple clusters of real-life scale-out application workloads based web search and 2) utilization traces obtained from real datacenter setups. According to our experiments, the proposed solution provides up to 13.7% power savings with up to 15.6% improvement of Quality-of-Service (QoS) compared to existing correlation-aware VM allocation schemes for datacenters.

ACM and IEEE Press2013

Chargement

Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centers

David Atienza Alonso, Ali Pahlevan, Xiaoyu Qu, Marina Zapater Sancho

Modern cloud data centers (DCs) need to tackle efficiently the increasing demand for computing resources and address the energy efficiency challenge. Therefore, it is essential to develop resource provisioning policies that are aware of virtual machine (VM) characteristics, such as CPU utilization and data communication, and applicable in dynamic scenarios. Traditional approaches fall short in terms of flexibility and applicability for large-scale DC scenarios. In this paper we propose a heuristic- and a machine learning (ML)-based VM allocation method and compare them in terms of energy, quality of service (QoS), network traffic, migrations, and scalability for various DC scenarios. Then, we present a novel hyper-heuristic algorithm that exploits the benefits of both methods by dynamically finding the best algorithm, according to a user-defined metric. For optimality assessment, we formulate an integer linear programming (ILP)-based VM allocation method to minimize energy consumption and data communication, which obtains optimal results, but is impractical at runtime. Our results demonstrate that the ML approach provides up to 24% server-to-server network traffic improvement and reduces execution time by up to 480x compared to conventional approaches, for large-scale scenarios. On the contrary, the heuristic outperforms the ML method in terms of energy and network traffic for reduced scenarios. We also show that the heuristic and ML approaches have up to 6% energy consumption overhead compared to ILP-based optimal solution. Our hyper-heuristic integrates the strengths of both the heuristic and the ML methods by selecting the best one during runtime.

2017

Chargement

Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centers

David Atienza Alonso, Ali Pahlevan, Xiaoyu Qu, Marina Zapater Sancho

2018

Chargement

A green data center, or sustainable data center, is a service facility which utilizes energy-efficient technologies. They do not contain obsolete systems (such as inactive or underused servers), and

Afficher plus